# if you installed the nico package then uncomment and load the respective modules 

#from nico import Annotations as sann
#from nico import Interactions as sint
#from nico import Covariations as scov

import Annotations as sann
import Interactions as sint
import Covariations as scov

#import scanpy as sc
#import gseapy
#import xlsxwriter

#import numpy as np
#import time
#import os

import matplotlib as plt 

plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
plt.rcParams['axes.linewidth'] = 0.1 #set the value globally

# please use Helvetica font according to your OS to make compatible with Adobe Illustrator. 
plt.rcParams['font.family'] = 'Helvetica'
plt.rcParams['font.sans-serif'] = ['Helvetica']

# Use the default font for all the figures 
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Tahoma', 'DejaVu Sans','Lucida Grande', 'Verdana']

import warnings
warnings.filterwarnings("ignore")

Usage introduction

For details of the function usage and input parameters either refer to the documentation or
just write the function and add .doc to retrieve infromation on all relelvant parameters.

print(sann.find_anchor_cells_between_ref_and_query.doc)
print(sint.spatial_neighborhood_analysis.doc)
print(scov.gene_covariation_analysis.doc)

All the figures will be saved in saveas=pdf format as vector graphics by default.
For every function that generates figures, the following default parameters are used:
transparent_mode=False, saveas='pdf',showit=True, dpi=300

For saving figures in png format, set saveas='png'
For generating images without background, set transparent_mode=True
If figure outputs within the Jupyter Notebook is not desired, set showit=False

Please download the sample data from the git
repository https://github.com/ankitbioinfo/nico_tutorial and
keep all the files and folders in the same directory to complete the tutorial.

inputRef (single-cell RNA-sequencing data)
inputQuery (single-cell resolution spatial transcriptomics data)
NiCoLRdb.txt (Ligand-receptor database file)

Before running the Perform_spatial_analysis.ipynb notebook,
input data need to be prepared by running
the Start_Data_preparation_for_niche_analysis.ipynb notebook
Once all the steps of Start_Data_preparation_for_niche_analysis.ipynb are completed,
the following commands can be executed to run a complete NiCo aanlysis.

#parameters for saving plots
saveas='png'
transparent_mode=False

ref_datapath='./inputRef/'
query_datapath='./inputQuery/'



output_nico_dir='./nico_out/'
output_annotation_dir=None #uses default location
#output_annotation_dir=output_nico_dir+'annotations/'
annotation_save_fname= 'nico_celltype_annotation.h5ad'
inputRadius=0

The cell type cluster slot in scRNAseq data is ref_cluster_tag
For example in AnnData object the location of slot: adata.obs['cluster']
In this slot cell type annotation of scRNAseq data is stored

ref_cluster_tag='cluster' #scRNAseq cell type slot 
annotation_slot='nico_ct' #spatial cell type slot 

Module A: Perform cell type annotation of spatial data

First find anchored cells between two modalities

anchors_and_neighbors_info=sann.find_anchor_cells_between_ref_and_query(
refpath=ref_datapath,
quepath=query_datapath,
output_nico_dir=output_nico_dir,
output_annotation_dir=output_annotation_dir)

Selection of parameters

Perform NiCo cell type annotation using the spatial Leiden cluster from the
Start_Data_preparation_for_niche_analysis.ipynb notebook,
e.g., with Leiden resolution parameter 0.4

If you have a large number of cells (>200,000) and want to perform
cell type annotation with different Leiden resolution parameters or
any other parameters, save the output_annotation_dir directory with
a different name for each run.

MNN (Mutual Nearest Neighbors) alignment takes a considerable amount
of time, which can delay the analysis in ordinary laptop. Therefore, it is
advisable to save the anchors_data_50.npz file, as the anchor information is
independent of the resolution parameter.

The annotation slot in scRNAseq data and initial cluster slot in spatial data

ref_cluster_tag='cluster'

ref_cluster_tag define the cellType slot for scRNAseq data.

Example .obs['cluster']

If scRNAseq cellType is stored with some other slot then please change the slot name

guiding_spatial_cluster_resolution_tag='leiden0.4'
guiding_spatial_cluster_resolution_tag define the Leiden clustering slot for spatial data.
Example .obs['leiden0.4']

output_info=sann.nico_based_annotation(anchors_and_neighbors_info,
guiding_spatial_cluster_resolution_tag='leiden0.4',
across_spatial_clusters_dispersion_cutoff=0.15,
resolved_tie_issue_with_weighted_nearest_neighbor='No')

This function sann.delete_files deletes the anchored file details
created in the find_anchor_cells_between_ref_and_query step.
If you have a large number of cells and want to experiment different
annotation parameters, do not delete this file as it can save a
significant amount of time.

sann.delete_files(output_info)

# Visualize the anchor cells between two modalities. 
# sann.visualize_spatial_anchored_cell_mapped_to_scRNAseq(output_info)

Save the annotation file into AnnData object

Save the annotation file into AnnData object (annotation_save_fname)
along with given expression matrix in "output_nico_dir" directory.

sann.save_annotations_in_spatial_object(output_info,
anndata_object_name=annotation_save_fname)

Nico based cluster annotations are saved in .obs['nico_ct'] slot

Annotations from different computational methods such cell2location or TACCO

If user have an AnnData object with cell type annotations
from different methods, you can skip the previous steps.

To use your own annotations, provide the following files:
annotation_save_fname= 'nico_celltype_annotation.h5ad'

Replace the annotation_save_fname AnnData object
with your own AnnData object containing the annotations.
Ensure that the annotation slot name in your AnnData object
is adjusted to match the following:

annotation_slot='nico_ct'

This will ensure compatibility with the NiCo pipeline.

Visualize the spatial annotations of all cell types

Left side: tissue map, Right side: UMAP

sann.visualize_umap_and_cell_coordinates_with_all_celltypes( 
output_nico_dir=output_nico_dir,
output_annotation_dir=output_annotation_dir,
anndata_object_name=annotation_save_fname,
#spatial_cluster_tag='nico_ct',
spatial_cluster_tag=annotation_slot,    
spatial_coordinate_tag='spatial',
umap_tag='X_umap',
saveas=saveas,transparent_mode=transparent_mode)

The figures are saved:  ./nico_out/annotations/tissue_and_umap_with_all_celltype_annotations.png

Visualize spatial annotations of selected pairs (or larger sets) of cell types

Left side: tissue map, Right side: UMAP

choose_celltypes=[['Stem/TA','Paneth'],['Paneth','Goblet']]

sann.visualize_umap_and_cell_coordinates_with_selected_celltypes(
choose_celltypes=choose_celltypes,    
output_nico_dir=output_nico_dir,
output_annotation_dir=output_annotation_dir,
anndata_object_name=annotation_save_fname,
spatial_cluster_tag=annotation_slot,spatial_coordinate_tag='spatial',umap_tag='X_umap',
saveas=saveas,transparent_mode=transparent_mode)

The figures are saved:  ./nico_out/annotations/fig_individual_annotation/Stem_TA0.png
The figures are saved:  ./nico_out/annotations/fig_individual_annotation/Paneth1.png

# For visualizing every cell type individually, leave list choose_celltypes list empty.  
#sann.visualize_umap_and_cell_coordinates_with_selected_celltypes(choose_celltypes=[])

Module B: Infer significant niche cell type interactions

Radius definition

The radius R=0 in NiCo incorporates the neighboring cells that are in immediate contact with
the central cell to construct the expected neighborhood composition matrix.
We envision NiCo as a method to explore direct interactions with physical neighbors
(R=0), but in principle finite distance interactions mediated by diffusive factors could be
explored by increasing R and comparing to the interactions obtained with R=0.

It may be helpful to explore a larger radius if it is expected that cell types interact through long-range
interactions. However, during the covariation task immediate neighbors typically capture the
strongest signal, while a larger radius averages the signal from a more significant number of
cells, potentially diluting the signal. Therefore, we recommend running NiCo with R=0.

Perform neighborhood analysis across direct neighbors
(juxtacrine signaling, R=0) of the central niche cell type.

To exclude cell types from the neighborhood analysis,
add celltype names to the list removed_CTs_before_finding_CT_CT_interactions.

In the example below, the cell types Basophils, Cycling/GC B cell,
and pDC, would not be included in the niche interaction task due to their low abundance.

do_not_use_following_CT_in_niche=['Basophils','Cycling/GC B cell','pDC']

niche_pred_output=sint.spatial_neighborhood_analysis(
Radius=inputRadius,
output_nico_dir=output_nico_dir,
anndata_object_name=annotation_save_fname,
spatial_cluster_tag='nico_ct',
removed_CTs_before_finding_CT_CT_interactions=do_not_use_following_CT_in_niche)

average neighbors: 4.83637851104445
average distance: 64.08306688807858
data shape (7305, 19) (7305,) neighbor shape (7305, 17)
Searching hyperparameters  Grid method: 0.015625
Searching hyperparameters  Grid method: 0.0078125
Searching hyperparameters  Grid method: 0.0078125
Inverse of lambda regularization found 0.0078125
training (5844, 17) testing (1461, 17) coeff (17, 17)

# this cutoff is use for the visualization of cell type interactions network 
celltype_niche_interaction_cutoff=0.1

In some computing machine pygraphviz is not able to load the neato package automatically.
In such case please define the location of the neato package.
If you install pygraphviz through conda conda install -c conda-forge pygraphviz
then most likely it should work.

import pygraphviz
a=pygraphviz.AGraph()
a._get_prog('neato')

import os
if  not '/home/[username]/miniforge3/envs/SC/bin/' in os.environ["PATH"]:
    os.environ["PATH"] += os.pathsep + '/home/[username]/miniforge3/envs/SC/bin/'

# Plot the niche interaction network without any edge weight details for cutoff 0.1 

sint.plot_niche_interactions_without_edge_weight(niche_pred_output,
niche_cutoff=celltype_niche_interaction_cutoff,
saveas=saveas,transparent_mode=transparent_mode)

The figures are saved:  ./nico_out/niche_prediction_linear/Niche_interactions_without_edge_weights_R0.png

# Plot the niche interaction network with edge weight details for cutoff 0.1

sint.plot_niche_interactions_with_edge_weight(niche_pred_output,
niche_cutoff=celltype_niche_interaction_cutoff,
saveas=saveas,transparent_mode=transparent_mode)

The figures are saved:  ./nico_out/niche_prediction_linear/Niche_interactions_with_edge_weights_R0.png

Cell type niche plot individually

Order niche cell types by magnitude of regression coefficients,
add celltype names to the list argument choose_celltypes,
e.g., for the Stem/TA and Paneth cell niche.

# Blue dotted line in following plot is celltype_niche_interaction_cutoff

sint.find_interacting_cell_types(niche_pred_output,
choose_celltypes=['Stem/TA','Paneth'],
celltype_niche_interaction_cutoff=celltype_niche_interaction_cutoff,
saveas=saveas,transparent_mode=transparent_mode,figsize=(4.0,2.0))

The figures are saved:  ./nico_out/niche_prediction_linear/TopCoeff_R0/Rank1_Paneth.png
The figures are saved:  ./nico_out/niche_prediction_linear/TopCoeff_R0/Rank3_Stem_TA.png

If niche cell types from the niche neighborhood of all central
cell types should be plotted or saved,
then leave choose_celltypes list argument empty.

#sint.find_interacting_cell_types(niche_pred_output,choose_celltypes=[])

# Plot the ROC curve of the classifier prediction for one of the crossfolds. 
# sint.plot_roc_results(niche_pred_output,saveas=saveas,transparent_mode=transparent_mode))
# sint.plot_predicted_probabilities(niche_pred_output)

Plot the average confusion matrix of the classifier from cross-folds

sint.plot_confusion_matrix(niche_pred_output,
saveas=saveas,transparent_mode=transparent_mode)

The figures are saved:  ./nico_out/niche_prediction_linear/Confusing_matrix_R0.png

Plot the average coefficient matrix of the classifier from cross-folds

sint.plot_coefficient_matrix(niche_pred_output,
saveas=saveas,transparent_mode=transparent_mode)

The figures are saved:  ./nico_out/niche_prediction_linear/weight_matrix_R0.png

Plot the evaluation score of the classifier for different metrics

sint.plot_evaluation_scores(niche_pred_output, 
saveas=saveas, transparent_mode=transparent_mode,
figsize=(4,3))

The figures are saved:  ./nico_out/niche_prediction_linear/scores_0.png

Module C: Perform niche cell state covariation analysis using latent factors

Note: From module C onwards, Jupyter cells are independent of previous steps.
Therefore, if you want to try different settings, you do not need to run the previous Jupyter cells.

Covariations parameters settings

Infer desired number of latent factors (e.g., no_of_factors=3) for each cell type
from both modalities using integrated non-negative matrix factorization.
Set iNMFmode=False for applying the conventional
non-negative matrix factorization method.
In this case, latent factors will be derived from the scRNA-seq data
and transfered to the spatial modality.

This option is preferable if spatial data are affected by substantial
technical noise due to unspecific background signal
or gene expression spill-over between neighboring
cell types due to imperfect segmentation.

Ligand-Receptor database file

NiCoLRdb.txt is the name of the ligand-receptor database file.
Users can use databases of similar format from any resource.

NiCoLRdb.txt was created by merging ligand-receptor pairs from NATMI, OMNIPATH, and CellPhoneDB.
User can download this database from github
and put into local directory
from where this notebook is getting run.

# By default, it run in spatial_integration_modality='double' that means 
# it integrates spatial transcriptomics with scRNAseq data modalities 

cov_out=scov.gene_covariation_analysis(iNMFmode=True,
        Radius=inputRadius,
        no_of_factors=3,
        refpath=ref_datapath,
        quepath=query_datapath,
        spatial_integration_modality='double',
        output_niche_prediction_dir=output_nico_dir,
        ref_cluster_tag=ref_cluster_tag) #LRdbFilename='NiCoLRdb.txt'

common genes between sc and sp 203 203


 Spatial and scRNA-seq number of clusters, respectively  17 19
Common cell types between spatial and scRNA-seq data   17 {'cDC/monocyte', 'neurons/enteroendocrine', 'Lymphatic', 'Plasma', 'Stroma', 'Tuft', 'Macrophage', 'Goblet', 'Glial', 'Blood vasc.', 'Paneth', 'MZE', 'T cell', 'TZE', 'Rest B', 'BZE', 'Stem/TA'}

The spatial cluster name does not match the scRNA-seq cluster name  set()
If the above answer is Null, then everything is okay. However, if any spatial cell type does not exist in the scRNA-seq data, please correct this manually; otherwise, NiCo will not run. 



BZE alpha, H size, W size, spH size: 30 (3, 325) (120, 3) (3, 1639)
Blood vasc. alpha, H size, W size, spH size: 28 (3, 33) (58, 3) (3, 148)
Glial alpha, H size, W size, spH size: 4 (3, 10) (44, 3) (3, 96)
Lymphatic alpha, H size, W size, spH size: 24 (3, 267) (97, 3) (3, 1301)
MZE alpha, H size, W size, spH size: 2 (3, 63) (60, 3) (3, 111)
Macrophage alpha, H size, W size, spH size: 16 (3, 89) (113, 3) (3, 346)
Paneth alpha, H size, W size, spH size: 12 (3, 128) (127, 3) (3, 184)
Plasma alpha, H size, W size, spH size: 16 (3, 85) (101, 3) (3, 439)
Rest B alpha, H size, W size, spH size: 12 (3, 234) (71, 3) (3, 48)
Stem/TA alpha, H size, W size, spH size: 8 (3, 420) (140, 3) (3, 1131)
Stroma alpha, H size, W size, spH size: 6 (3, 84) (107, 3) (3, 271)
T cell alpha, H size, W size, spH size: 46 (3, 54) (86, 3) (3, 488)
TZE alpha, H size, W size, spH size: 8 (3, 40) (72, 3) (3, 340)
Tuft alpha, H size, W size, spH size: 40 (3, 90) (68, 3) (3, 25)
cDC/monocyte alpha, H size, W size, spH size: 26 (3, 40) (86, 3) (3, 76)
neurons/enteroendocrine alpha, H size, W size, spH size: 2 (3, 26) (103, 3) (3, 250)

Visualize the cosine similarity and Spearman correlation between genes and latent factors

Following function generates output for the top 30 genes
based on cosine similarity (left) or Spearman correlation (right)

Select cell types by adding IDs to list argument choose_celltypes,
or leave empty for generating output for all cell types

scov.plot_cosine_and_spearman_correlation_to_factors(cov_out,
choose_celltypes=['Paneth'],
NOG_Fa=30,saveas=saveas,transparent_mode=transparent_mode,
figsize=(15,10))

cell types found  ['Paneth']
The figures are saved:  ./nico_out/covariations_R0_F3/NMF_output/Paneth.png

#Cosine and spearman correlation: visualize the correlation of genes from NMF
scov.plot_cosine_and_spearman_correlation_to_factors(cov_out,
choose_celltypes=['Stem/TA'],
NOG_Fa=30,saveas=saveas,transparent_mode=transparent_mode,
figsize=(15,10))

cell types found  ['Stem/TA']
The figures are saved:  ./nico_out/covariations_R0_F3/NMF_output/Stem_TA.png

Visualize genes in the latent factors along with average expression

Call following function (scov.extract_and_plot_top_genes_from_chosen_factor_in_celltype) to
visualize correlation and expression of genes associated with factors

For example, visualize and extract the top 10 genes (top_NOG=20)
correlating negatively (positively_correlated=False)
by Spearman correlation (correlation_with_spearman=True)
for cell type Stem/TA (choose_celltype='Stem/TA')
in factor 1 (choose_factor_id=1)

dataFrame=scov.extract_and_plot_top_genes_from_chosen_factor_in_celltype(
cov_out,
choose_celltype='Stem/TA',
choose_factor_id=1,
top_NOG=20,
correlation_with_spearman=True,
positively_correlated=False,
saveas=saveas,transparent_mode=transparent_mode )

The figures are saved:  ./nico_out/covariations_R0_F3/dotplots/Factors_Stem_TA.png

Visualize the latent factor values and proportion of population expressed that gene

inspect the top genes in the given factor as in table
proportion_of_population_expressed:
proportion of cells expressing a gene in the respective cluster

dataFrame

	Gene	Fa	mean_expression	proportion_of_population_expressed
0	Chp2	-0.626481	1.619048	0.388095
1	Rbp7	-0.623792	3.402381	0.504762
2	Lgals3	-0.584694	2.847619	0.480952
3	St3gal4	-0.575894	3.750000	0.492857
4	Gm3336	-0.563401	1.152381	0.383333
5	Coro2a	-0.561060	2.904762	0.657143
6	Dhrs11	-0.558811	1.773810	0.585714
7	Akr1c19	-0.556204	1.142857	0.359524
8	Cdkn2b	-0.555436	0.973810	0.257143
9	Serpinb6a	-0.550037	7.459524	0.895238
10	Slc51a	-0.549629	1.123810	0.333333
11	Anxa2	-0.545655	5.378572	0.761905
12	Smim24	-0.544530	11.040476	0.945238
13	Apol10a	-0.541590	1.271429	0.297619
14	Cyp4f40	-0.535966	0.733333	0.326190
15	Car4	-0.535653	2.238095	0.464286
16	Mall	-0.524968	0.778571	0.361905
17	Anxa13	-0.524648	2.526191	0.621429
18	Pfkp	-0.520550	1.642857	0.483333
19	2200002D01Rik	-0.519799	8.476191	0.911905

Save the latent factors into excel sheet

save data in an Excel sheet for each cell type,
including latent factor associations of all genes according to
Spearman correlation and cosine similarity.

scov.make_excel_sheet_for_gene_correlation(cov_out)

Module D: Cell type covariation visualization

Plot covariations between niche cell types (x-axis) and central cell type
(y-axis, defined by list argument choose_celltypes).

Circle size scales with -log10(p-value) (indicated as number on top of each circle).
To generate plots for all cell types, leave list argument choose_celltypes empty.

choose_celltypes=['Stem/TA']
scov.plot_significant_regression_covariations_as_circleplot(cov_out,
choose_celltypes=choose_celltypes, 
mention_pvalue=True,
saveas=saveas,transparent_mode=transparent_mode,
figsize=(6,1.25))

cell types found  ['Stem/TA']
The regression figures as pvalue circle plots are saved in following path  ./nico_out/covariations_R0_F3/Regression_outputs/pvalue_coeff_circleplot_*

In the following example, a p-value cutoff is explicitely defined by the pvalue_cutoff argument and -log10(p-value) is not printed on top of circels.

choose_celltypes=['Stem/TA']

scov.plot_significant_regression_covariations_as_circleplot(cov_out,
choose_celltypes=choose_celltypes,
pvalue_cutoff=0.05,mention_pvalue=False,
saveas=saveas,transparent_mode=transparent_mode,
figsize=(6,1.25))

cell types found  ['Stem/TA']
The regression figures as pvalue circle plots are saved in following path  ./nico_out/covariations_R0_F3/Regression_outputs/pvalue_coeff_circleplot_*

Visualize as heatmap instead of circle plot

Plot covariations between niche cell types (x-axis) and central cell type
(y-axis, defined by list argument choose_celltypes) as heatmap.

Leave list argument choose_celltypes empty to generate plots for all cell types.
The top subfigure shows the coefficient and bottom subfigure shows the -log10 p-value.

scov.plot_significant_regression_covariations_as_heatmap(cov_out,
choose_celltypes=['Stem/TA'],
saveas=saveas,transparent_mode=transparent_mode, figsize=(6,1.25))

cell types found  ['Stem/TA']
The regression figures as pvalue heatmap plots are saved in following path  ./nico_out/covariations_R0_F3/Regression_outputs/pvalue_coeff_heatmap_*

Module E: Analysis of ligand-receptor interactions within the cell type covariation state

Save excelsheets and summary in text file

Save all ligand-receptor interactions infered for the niche of each cell type niche in an Excel sheet,
and a summary of significant niche interactions in a text file.

scov.save_LR_interactions_in_excelsheet_and_regression_summary_in_textfile_for_interacting_cell_types(cov_out,
pvalueCutoff=0.05,correlation_with_spearman=True,
LR_plot_NMF_Fa_thres=0.1,LR_plot_Exp_thres=0.1,number_of_top_genes_to_print=5)

The Excel sheet is saved:  ./nico_out/covariations_R0_F3/Lig_and_Rec_enrichment_in_interacting_celltypes.xlsx
The text file is saved: ./nico_out/covariations_R0_F3/Regression_summary.txt

Usage for ligand receptor visualizations

Perform ligand-receptors analysis
In this example, output is generated for the ligand-receptor pairs associated with the intercting
factor 1 of Stem/TA cells and factor 1 of Paneth cells.

choose_interacting_celltype_pair=['Stem/TA','Paneth']
choose_factors_id=[1,1] entries correspond to cell types in
choose_interacting_celltype_pai, i.e.,
first factor ID corresponds to Stem/TA and second factor ID corresponds to Paneth

By default, the analysis is saved in 3 separate figures (bidirectional, CC to NC and NC to CC).
CC: central cell
NC: niche cell

Our analysis accounts for bidirectional cellular crosstalk interactions of ligands and receptors in cell types A and B.
The ligand can be expressed on cell type A and signal to the receptor detected on cell type B, or vice versa.

By changing the cutoff for minimum factor correlation of ligand/receptor genes (LR_plot_NMF_Fa_thres=0.2)
or the cutoff for the minimum fraction of cells expressing the ligand/receptor genes (LR_plot_Exp_thres=0.2) the
stringency of the output filtering can be controled.

scov.find_LR_interactions_in_interacting_cell_types(cov_out,
choose_interacting_celltype_pair=['Stem/TA','Paneth'],
choose_factors_id=[1,1],
pvalueCutoff=0.05,
LR_plot_NMF_Fa_thres=0.3,
LR_plot_Exp_thres=0.2,
saveas=saveas,transparent_mode=transparent_mode,figsize=(12, 10))

LR figures for both ways are saved in following path  ./nico_out/covariations_R0_F3/Plot_ligand_receptor_in_niche/
LR figures for CC to NC are saved in following path  ./nico_out/covariations_R0_F3/Plot_ligand_receptor_in_niche_cc_vs_nc/
LR figures for NC to CC are saved in following path  ./nico_out/covariations_R0_F3/Plot_ligand_receptor_in_niche_nc_vs_cc/

0

Perform ligand-receptors analysis of the Paneth cell niche
including all significant interaction partners.
choose_interacting_celltype_pair=['Paneth'] generate
plots for all cell types interacting sigificantly with Paneth cells
choose_factors_id=[] if empty, generate plots
for all significantly covarying factors

scov.find_LR_interactions_in_interacting_cell_types(cov_out,
choose_interacting_celltype_pair=['Paneth'],
choose_factors_id=[], 
LR_plot_NMF_Fa_thres=0.2,
LR_plot_Exp_thres=0.2,
saveas=saveas,transparent_mode=transparent_mode,figsize=(12, 10))

LR figures for both ways are saved in following path  ./nico_out/covariations_R0_F3/Plot_ligand_receptor_in_niche/
LR figures for CC to NC are saved in following path  ./nico_out/covariations_R0_F3/Plot_ligand_receptor_in_niche_cc_vs_nc/
LR figures for NC to CC are saved in following path  ./nico_out/covariations_R0_F3/Plot_ligand_receptor_in_niche_nc_vs_cc/

0

Module F: Perform functional enrichment analysis for genes associated with latent factors

Perform pathway enrichment analysis for factor-associated genes

In this example, pathway analysis is performed for top 50 (NOG_pathway=50) genes,
positively correlated (positively_correlated=True) with
factor 1 (choose_factors_id=[2]) of Stem/TA cells (choose_celltypes=['Stem/TA'])
testing for enrichment of GO Biological Processes (database=['GO_Biological_Process_2021']).

If savefigure=True, then the figures will be saved in the respective folder.

scov.pathway_analysis(cov_out,
choose_celltypes=['Stem/TA'],
NOG_pathway=50,
choose_factors_id=[2],
positively_correlated=True,
savefigure=False,
database=['GO_Biological_Process_2021'])

The pathway figures are saved in  ./nico_out/covariations_R0_F3/Pathway_figures/
cell types found  ['Stem/TA']

In this example, pathway analysis is performed for top 50 (NOG_pathway=50) genes,
negatively correlated (positively_correlated=False)
with factor 1 (choose_factors_id=[2])
of Stem/TA cells (choose_celltypes=['Stem/TA'])
testing for enrichment of GO Biological Processes (database=['GO_Biological_Process_2021']).

If savefigure=True, then the figures will be saved in the respective folder.

scov.pathway_analysis(cov_out,
choose_celltypes=['Stem/TA'],
NOG_pathway=50,
choose_factors_id=[2],
positively_correlated=False,
savefigure=False,
database=['GO_Biological_Process_2021'])

The pathway figures are saved in  ./nico_out/covariations_R0_F3/Pathway_figures/
cell types found  ['Stem/TA']

In this example, pathway analyses are performed for top 50 (NOG_pathway=50) genes,
positively correlated (positively_correlated=True)
with any factor (choose_factors_id=[])
of Paneth cells (choose_celltypes=['Paneth'])
ribosome and mitochondrial genes are not included in the gene list
testing for enrichment of pathways from three databases

(GO_Biological_Process_2021, BioPlanet_2019, Reactome_2016).

scov.pathway_analysis(cov_out,
choose_celltypes=['Paneth'],
NOG_pathway=50,
choose_factors_id=[],
positively_correlated=True,
savefigure=False,
rps_rpl_mt_genes_included=False)

The pathway figures are saved in  ./nico_out/covariations_R0_F3/Pathway_figures/
cell types found  ['Paneth']

In this example, pathway analysis is performed for top 50 (NOG_pathway=50) genes,
negatively correlated (positively_correlated=False)
with factor 2 (choose_factors_id=[2])
of Goblet cells (choose_celltypes=['Goblet'])
testing for enrichment of BioPlanet pathways (database=['BioPlanet_2019']).

If savefigure=True, then the figures will be saved in the respective folder.

scov.pathway_analysis(cov_out,
choose_celltypes=['Goblet'],
NOG_pathway=50,choose_factors_id=[2],
positively_correlated=False,
savefigure=False,
database=['BioPlanet_2019'])

The pathway figures are saved in  ./nico_out/covariations_R0_F3/Pathway_figures/
cell types found  ['Goblet']

Module G: Visualization of top genes across cell type and factors as dotplot

Show the top 20 positively and negatively correlated genes (top_NOG=20) for all latent factors and
the average expression of these genes on a log scale in a single plot.
In this example, plots are generated for Paneth cells.

If the choose_celltypes=[], the plot will be generated for all cell types.

scov.plot_top_genes_for_a_given_celltype_from_all_three_factors(
cov_out,choose_celltypes=['Paneth','Stem/TA'],
top_NOG=20,saveas=saveas,transparent_mode=transparent_mode)
                        

cell types found  ['Paneth', 'Stem/TA']
The figures are saved:  ./nico_out/covariations_R0_F3/dotplots/Paneth.png
The figures are saved:  ./nico_out/covariations_R0_F3/dotplots/Stem_TA.png

scov.plot_top_genes_for_pair_of_celltypes_from_two_chosen_factors(cov_out,
choose_interacting_celltype_pair=['Stem/TA','Paneth'],
visualize_factors_id=[1,1],
top_NOG=20,saveas=saveas,transparent_mode=transparent_mode)

The figures are saved:  ./nico_out/covariations_R0_F3/dotplots/combined_Stem_TA_Paneth.png

Module H: Visualize factor values in the UMAP

Visualize factor values for select cell types, e.g.,
Stem/TA and Paneth cells (choose_interacting_celltype_pair=['Stem/TA','Paneth']) in scRNA-seq data umap.
Select factors for each cell type (visualize_factors_id=[1,1]).

List entries correspond to cell types in choose_interacting_celltype_pair.

scov.visualize_factors_in_scRNAseq_umap(cov_out,
choose_interacting_celltype_pair=['Stem/TA','Paneth'],
visualize_factors_id=[1,1],
saveas=saveas,transparent_mode=transparent_mode,figsize=(8,3.5))

The figures are saved:  ./nico_out/covariations_R0_F3/scRNAseq_factors_in_umap.png

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Visualize factor values for select cell types, e.g., Stem/TA and Paneth cells (choose_interacting_celltype_pair=['Stem/TA','Paneth']) in spatial transcriptomics data umap. Select factors for each cell type (visualize_factors_id=[1,1]). List entries correspond to cell types in choose_interacting_celltype_pair.

scov.visualize_factors_in_spatial_umap(cov_out,
visualize_factors_id=[1,1],
choose_interacting_celltype_pair=['Stem/TA','Paneth'],
saveas=saveas,transparent_mode=transparent_mode,figsize=(8,3.5))

The figures are saved:  ./nico_out/covariations_R0_F3/spatial_factors_in_umap.png

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#For visualization of one cell type at a time only 


scov.visualize_factors_in_spatial_umap(cov_out, 
visualize_factors_id=[2],
choose_interacting_celltype_pair=['Stem/TA'],
saveas=saveas,transparent_mode=transparent_mode,figsize=(4,3.5))

scov.visualize_factors_in_scRNAseq_umap(cov_out,
#refpath=ref_datapath,
choose_interacting_celltype_pair=['Stem/TA'],
visualize_factors_id=[2],
saveas=saveas,transparent_mode=transparent_mode,figsize=(4,3.5))

The figures are saved:  ./nico_out/covariations_R0_F3/spatial_factors_in_umap.png
The figures are saved:  ./nico_out/covariations_R0_F3/scRNAseq_factors_in_umap.png

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